You’re comparing Weld vs SnapLogic vs StreamSets Data Collector. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | SnapLogic | StreamSets Data Collector |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Subscription (connector & usage-based; starts ~$50k/year) | Free OSS Data Collector; enterprise DataOps Platform is custom-priced |
| Free tier | No | No | Yes |
| Location | DK, (EU) | San Mateo, CA, USA | San Francisco, CA, USA |
| Connectors & Sync | |||
| Connectors | 200+ | 500+ | 200+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | Yes | No |
| Two-Way Sync | Yes | Yes | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | Yes | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | Yes | Yes |
| Version control | Yes | Yes | Yes |
| On-Premise | No | No | Yes |
| OpenAPI / Developer API | Yes | Yes | No |
| Integrations | |||
| Load to/from Excel | Yes | Yes (via Snaps) | Yes (via file connectors) |
| Load to/from Google Sheets | Yes | Yes (Google Sheets Snap) | No |
| Ratings | |||
| G2 rating | 4.8 | 4.4 | 4.5 |
Overview
Weld is a unified ELT and data activation platform that combines ingestion, modeling, transformations, orchestration, lineage, and reverse ETL in a single SaaS interface. With premium in-house–built connectors, an intuitive UI, and near real-time syncs, Weld enables both technical and non-technical users to create and manage data workflows efficiently. Weld also includes an AI assistant to support SQL modeling, generate transformations, and streamline repetitive tasks. Teams can ingest data from a wide range of sources—including marketing platforms, CRMs, databases, Google Sheets, Excel, and APIs—into their cloud data warehouse and activate it back into business tools.
Lineage, orchestration, and workflow features included by default
Handles large datasets and near real-time data sync
ELT and reverse ETL in one platform
User-friendly interface with minimal setup required
Flat, predictable monthly pricing model
200+ in-house–built, high-quality connectors
AI assistant for modeling and transformations
Some SQL knowledge is useful for advanced modeling
Optimized for cloud-warehouse workflows (Snowflake, BigQuery, Redshift, etc.)
Feature set is streamlined for modern ELT/activation use cases
A reviewer on G2 said:
Overview
SnapLogic is an Integration Platform as a Service (iPaaS) that supports ETL, ELT, application integration, and API management using its visual Snap-based architecture. It includes 500+ pre-built Snap connectors for SaaS applications, databases, on-prem systems, and big data platforms. Pipelines are designed in a drag-and-drop interface (Snap Studio) and run on a fully managed, autoscaling cloud environment. SnapLogic also provides AI-assisted pipeline building through Iris, its AI recommendation engine.
500+ Snap connectors covering SaaS, databases, big data, and on-prem sources.
Visual pipeline designer (Snap Studio) with AI-driven suggestions (Iris) for mappings and transformations.
Serverless execution with autoscaling and multi-cloud support (AWS, Azure, GCP).
Supports both batch and real-time streaming integrations.
Premium pricing can be costly for smaller organizations.
Designer UI may feel cluttered in very large pipelines, with occasional performance slowdowns.
Limited self-hosted options; primarily a SaaS platform.
Gartner Peer Review:
“Overall I was able to create pipelines required easily to migrate and fill data manually, which helped me a lot and improved my performance.”
“During development random bugs are appearing and there is mismatch with documentation.”
Overview
StreamSets Data Collector is an open-source data integration engine designed for continuous ingestion, transformation, and delivery. It supports both streaming systems such as Kafka and Kinesis, and batch sources including JDBC and file systems. Pipelines are built using a drag-and-drop canvas, and a key differentiator is Schema Drift Detection, which helps pipelines adapt automatically as input schemas evolve. Commercial editions extend the platform with enterprise monitoring, governance, metadata, and lineage features.
Schema Drift Detection adjusts dynamically to changes in incoming data schemas.
Supports streaming and batch ingestion within the same pipeline.
Visual pipeline builder with 200+ processors and connectors.
Open-source core available; enterprise offering adds monitoring, lineage, and governance.
Open-source version lacks enterprise monitoring, lineage, and governance.
UI performance can degrade with very large or complex pipelines.
Advanced pipeline logic often requires Groovy or Java scripting.
StreamSets Data Operations Platform:
“StreamSets’ ability to automatically detect and adapt to schema changes (drift) in streaming sources greatly reduces pipeline failures.”
“The open-source feature set is limited—monitoring, lineage, and enterprise support require the paid DataOps Platform. Debugging complex pipelines can be tricky if not familiar with the UI.”
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
SnapLogic’s Snap Studio offers a visual, drag-and-drop experience for building pipelines, with Iris AI suggesting mappings and transformations. While intuitive for most workflows, very large pipelines may feel crowded or slower to navigate.
StreamSets Data Collector provides a drag-and-drop canvas for assembling origin, processor, and destination stages. Schema drift is surfaced automatically. Simple pipelines are approachable, while advanced transformations may require scripting knowledge.
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
SnapLogic’s Snap Studio offers a visual, drag-and-drop experience for building pipelines, with Iris AI suggesting mappings and transformations. While intuitive for most workflows, very large pipelines may feel crowded or slower to navigate.
StreamSets Data Collector provides a drag-and-drop canvas for assembling origin, processor, and destination stages. Schema drift is surfaced automatically. Simple pipelines are approachable, while advanced transformations may require scripting knowledge.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
SnapLogic typically starts at around $50k per year for standard usage. Its pricing model based on connectors, features, and usage is aimed at mid-market and enterprise teams rather than smaller organizations.
The open-source Data Collector is free. Enterprise capabilities such as monitoring dashboards, lineage, and governance require licensing the DataOps Platform. Pricing varies based on deployments and enterprise features.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
SnapLogic typically starts at around $50k per year for standard usage. Its pricing model based on connectors, features, and usage is aimed at mid-market and enterprise teams rather than smaller organizations.
The open-source Data Collector is free. Enterprise capabilities such as monitoring dashboards, lineage, and governance require licensing the DataOps Platform. Pricing varies based on deployments and enterprise features.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Core features include 500+ Snaps, batch and streaming pipelines, AI-assisted design, API management, monitoring, multi-cloud deployment, and built-in data quality components.
Key features include schema drift detection, streaming and batch support, transformation processors, JDBC/Kafka/S3/HDFS connectors, enterprise monitoring and lineage (in paid edition), and containerized deployment.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Core features include 500+ Snaps, batch and streaming pipelines, AI-assisted design, API management, monitoring, multi-cloud deployment, and built-in data quality components.
Key features include schema drift detection, streaming and batch support, transformation processors, JDBC/Kafka/S3/HDFS connectors, enterprise monitoring and lineage (in paid edition), and containerized deployment.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
SnapLogic supports custom Snaps built in JavaScript or Python, parameterized pipelines, REST-triggered executions, and CI/CD integration. As a SaaS-only offering, it lacks fully self-hosted runtime options but provides strong extensibility within its cloud environment.
Custom processors can be written in Java or Groovy, and pipelines can be parameterized. StreamSets integrates with external orchestrators such as Airflow and monitoring tools like Prometheus or Grafana.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
SnapLogic supports custom Snaps built in JavaScript or Python, parameterized pipelines, REST-triggered executions, and CI/CD integration. As a SaaS-only offering, it lacks fully self-hosted runtime options but provides strong extensibility within its cloud environment.
Custom processors can be written in Java or Groovy, and pipelines can be parameterized. StreamSets integrates with external orchestrators such as Airflow and monitoring tools like Prometheus or Grafana.
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CUSTOMER STORIES
“One of the biggest impacts has been unlocking new ways to buy media. Before, we didn’t have the data to back up strategic decisions – now we do.”
“Before Weld, we had to rely on custom Python scripts and manual processes that were time-consuming and error-prone.”
“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”
“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”
“Before Weld, our data infrastructure was limited and we relied heavily on Excel files and custom Python scripts.”
“Weld has saved us a ton of time, from not having data ready to having a fully functional data warehouse and connectors.”
“By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance”
“We didn’t have a good data setup, so we lacked the business insights we needed. Weld has allowed us to set up a structured data infrastructure and access insights quickly.”
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